Talk by Ke Tao (from Web Information Systems, TU Delft) at 23rd ACM Conference on Hypertext and Social Media, June 28 2012, Milwaukee, WI, USA

1. Semantics + Filtering + Search = Twitcident
Exploring Information in Social Web Streams
Hypertext 2012, Milwaukee, WI – June 28
Fabian Abel, Claudia Hauff,
Geert-Jan Houben, Richard Stronkman, Ke Tao
Web Information Systems, TU Delft, the Netherlands
Delft
University of
Technology

2. 200,000,000
number of tweets published per day
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 2

3. Pukkelpop 2011
People tweet about everything,
everywhere :-)
3

4. 200,000,000
Pukkelpop 2011
became a tragedy
Filtering
Useful tweets? 81,000 tweets in four hours
Search &
Analytics
4

5. Case Nijmegen
Train accident
5

6. First tweet…
And then your train blasts off full of the
anvils. #Nijmegen #veolia
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 6

7. First picture…
Astonishing! My train rams the platform at
Nijmegen! http://pic.twitter.com/QVVfJHyd
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 7

8. Traditional news media
A train ramed the anvils at Nijmegen.
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 8

9. Research Challenges
1. (Automatic) Filtering: Given an incident, how can one
automatically identify those tweets that are relevant to
the incident?
2. Search & Analytics: How can one improve search and
analytical capabilities so that users can explore
information in the streams of tweets?
Search &
Filtering
Analytics
Twitter streams
topic information need
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 9

10. Search &
Analytics
Automatic
Filtering
Twitcident Pipeline
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 10

11. Twitcident system
! "#$% ( %6% , 8&#*( % ( , - . , ( / % % ( % ( ( $&
$5' . $"7 % + 7 $" $9
, 1%
? ' #)$% #%
+ >*(
! " #$% ' ( ) $&#*( % ( , - . , ( /%0( *% + ( 1%
#& % + . -
! "#$% %( . ' 2 % ' . *$% 4$%0( *% + ( 1%
/. ' ( *( #3. . -
:#;% #*)"%
<( :3;% 2 *( /% )7 % ( 3%$*( #+ %
=7$( <. #2! & :);% #2 )&
>, 5- %
F i gu r e 2: Scr een sh ot of t h e T w i t ci d en t sy st em : ( a) sear ch an d fi l t er i n g fu n ct i on al i t y t o ex p l or e an d r et r i ev e
p ar t i cu l ar T w i t t er m essages, ( b ) m essages t h at ar e r el at ed t o t h e gi ven i nci dent ( h er e: fi r es i n T ex as) an d
m at ch t h e Semanticsy+of t h e u ser+an d ( c) r ealTwitcident t-i cs of t h e m at ch i n g m essages.
gi ven qu er Filtering Search = t i m e an al y Exploring Information in Social Web Streams 11
In t he T wit cident syst em, bot h facet ed search and re- incident is det ect ed t hen t he T wit cident framework t rans-

12. Search &
Analytics
Automatic
Filtering
Twitcident Pipeline
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 12

13. Incident detection
Twitter
2.
P2000 Broadcast
•Twiticident relies on
Initial query:
(Moerdijk OR Chemie-Pack)
Broadcasted incident AND (fire OR smoke OR
Refined query based on
Emergency
(i) description: flame…) SINCE:2011-01-05
incident profiling:
Prio 1 fire : : Vlasweg : 4 4782PW 1. 3. (Moerdijk OR Dordrecht…) AND
Moerdijk :: Chemie Pack (#moerdijkFire OR toxic…)
Broadcasting Services
for detecting incidents.
Twitcident
Framework
4.
• In the Netherlands : P2000
communication network
(ii)
Incident in Twitcident:
Twitcident system
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 13

14. Incident Profiling
•For an incident i:
• The profile of an incident is
described as a set of tuples. Location,
0.4
Netherlands
• Each tuple includes a facet- Incident,
0.5
value pair (f, v) and its Train accident
weight to the incident i. Location,
0.8
Nijmegen
Orgranization,
0.6
Veolia
Incident,
1.0
Crash
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 14

15. Search &
Analytics
Automatic
Filtering
Twitcident Pipeline
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 15

16. Social Media Aggregation
• Collecting Twitter messages, pictures, and
videos from Social Media Platforms e.g. Twitter,
PhotoBucket, Vimeo
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 16

17. Search &
Analytics
Automatic
Filtering
Twitcident Pipeline
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 17

18. Semantic Enrichment
•Named Entity Recognition
•Classification : Casualties, Damages, Risks…
•Linkage : External Resources
•Metadata extraction
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 18

19. Search &
Analytics
Automatic
Filtering
Twitcident Pipeline
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 19

20. Filtering
•Which tweets are relevant to the incidents?
• Preprocessing : Language detection
• Semantic Filtering : Compare tweet with P(i)
• Semantic Filtering with News Context
• P’(i) : P(i) complemented with f-v pairs from news
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 20

21. Search &
Analytics
Automatic
Filtering
Twitcident Pipeline
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 21

22. Faceted Search
•Strategies (ranking)
• Frequency-based
• Time-sensitive based
• Personalized
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 22

23. Real-time analytics
What type of things are mentioned in the tweets? Impact Area
What aspects are mentioned over time? What do people report about over
time?
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 23

24. Evaluation - Dataset
•Twitter corpus （TREC Microblog Track 2011
）
• 16 million tweets (Jan. 24th – Feb. 8th, 2011 )
• 4,766,901 tweets classified as English
• 6.2 million entity-extractions
•News (Same time period)
• 62 RSS News Feeds
• 13,959 News Articles
• 357,559 entity-extractions
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 24

25. Evaluation
For tweets Filtering (1/2)
! "( % ! "' &%
! "' % ! ") #%
! ") % ! "+$% G HI %
! "+% ! "$' %
! "#( % I J &! %
! "$%
! "&) % I J $! %
! "#% ! "$*%
! "&% ! "#$% ! "#) % K- 2/5%
5
! "&' % ! "&&% ! "&#%
!%
, - . /012% , - . /012% B/<- 50- C
4 %
346- 74 %
5 08% 346- 74 9 4 % D- E9 >7F% 5 74
5 08% 6: 346- 08%
; - 9 <%
=>06- ?6@ 4 /1>0%
/5A
Semantic strategies outperform the keyword-
based filtering regarding all metrics.
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 25

26. Evaluation
For tweets Filtering (2/2)
The semantic strategy is more robust and
achieves higher precisions for complex topics.
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 26

27. Evaluation
For Faceted Search (1/2)
+%
&#$- %
! "#$%
+01#*2"1% %
(1"3
! "*% ! "&' %
! "#$% () *+' #,%
! "' #%
! "' %
.! &&/% %
&"'
! ") %
! "( %
!%
,-. / 0. 1234567. 8% 67: 9 4
: 6; 567. 8% : 67: 9 4
6; 567. 8%
,62. 9 8% 6-2: % ,62. 9. 8% 6-2: %<. 3= >-8% 6-2: %
. 7. 7. 7.
with semantic enrichment without semantic enrichment
The semantic faceted search strategy improves
the search performance by 34.8% and 22.4%.
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 27

28. Evaluation
For Faceted Search (2/2)
! ") +% ! ") ' %
! ") % ! "#&%
! "#( %
! "#+%
! "#%
! ", +% EF +%
! ", % ! "#$% ! "#+% ! "#&% ! "' *% EF ' ! %
! "#) % ! "#' %
! "' +% ! ", +%
! "' % G HH%
! "' ( %
! "! +% ! "' , %
!%
%
%
.%
@%
7
7
2?
;
56.
0.
058
34
D3
>.
2
12
C:
=
0.
B3
</.
/0
.:
0A
-.
89
A3
with semantic enrichment without semantic enrichment
The strategies with semantic enrichment outperform
the strategy without semantic enrichment in
predicting the appropriate facet-values.
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 28
Adaptive Faceted Search on Twitter 3

29. Conclusions
• What we have done:
• Twitcident, a framework for filtering, searching, and
analyzing information about incidents that people publish in
their Social Web Streams
• What we have achieved:
• Better filtering of Twitter messages for a given incident.
• Better search for relevant information about an incident within
the filtered messages.
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 29

30. Thank you!
@wisdelft
http://twitcident.org
Ke Tao
@taubau
Semantics + Filtering + Search = Twitcident - Exploring Information in Social Web Streams 30